An examination of agonist and antagonist motor unit firing properties

The interactions between opposing muscle (i.e. agonist and antagonist) groups can be extremely complex, task-dependent, and are still poorly understood. To identify possible origins of the coordination between antagonistic muscle groups, the common or shared sources of neural input need to be understood. The assessment and manipulation of motor unit firing properties, such as synchronization, can provide information regarding the common inputs to opposing muscles. PURPOSE: The purpose of this study was to introduce various interventions to systematically manipulate both agonist and antagonist motor unit firing properties, and obtain a better understanding of the interactions between the two. METHODS: Muscle activity was detected from the biceps brachii ("agonist") and the triceps brachii ("antagonist") during isometric forearm flexions. The signals from these muscles were decomposed into individual motor unit action potential trains. Subsequently, various firing properties such as mean firing rate, recruitment threshold, and synchronization were calculated. On two separate visits, either the agonist or antagonist muscle was fatigued. During another two visits, either the agonist or antagonist muscle underwent 18 minutes of prolonged stretching, which has been shown to significantly desensitize proprioceptors. RESULTS: During co-activation, the antagonist demonstrated significant motor unit synchronization, but to a lesser extent when compared to the agonist. The antagonist also exhibited a substantially smaller recruitment threshold range and higher average firing rates. Fatigue of the agonist did not show any changes to antagonist motor unit firing properties, despite a significant increase in co-activation. Fatigue of the antagonists produced effects on the motor unit behavior of the agonist, such as decreased motor unit synchronization. It was suggested that group III and IV muscle afferents originating from the antagonist were responsible for the change to the agonist. The stretching interventions provided some mixed results, often providing non-uniform changes across motor unit types. For example, agonist low-threshold motor unit pairs demonstrated an increase in short-term synchronization after agonist stretching, but the high-threshold motor unit pairs exhibited a decrease in synchronization. Future studies to help answer follow-up questions were suggested

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research